Bibliographic record
Abstract
This article is based on paper SPE 136690 by the same author, which was presented at the 2010 SPE Progressing Cavity Pumps Conference, Edmonton, Canada, 12-14 September. To extend the run life of the pump while producing all available fluid is the goal of all progressing cavity pump (PCP) operators. The primary challenge is to do so without starving the pump and causing damage to the stator. The petroleum industry has been searching for years for a reliable way to control PCPs for pump-off. Several methods have been used, from monitoring torque to manual fluid levels. To date, none have been commercially successful. A method for controlling these wells has been developed combining wedge meter flow technology and microprocessor control of both electric motors through the use of variable frequency drives (VFDs) and hydraulic motors using proportional control valves. This method has proved accurate and reliable, extending run life while producing all available fluids. Combining this automated technology at the well with a web-based system that feeds back real-time data to a dedicated supervisory control and data acquisition (SCADA) host allows PCP technical experts to diagnose problems, and operators to respond quickly to changing well conditions. This article discusses the advances in automation and optimization of PCPs. The acceptance of PCPs in the oil and gas industry has grown worldwide. A major concern is potential damage to the stator if the well is pumped off. The most common prevention is to ensure that there is a substantial amount of fluid level above the pump. Operators taking manual fluid level shots and adjusting pump speed has been the most widely used method to maintain a safe fluid level. With advancements in fluid flow measurement and proven algorithms, the PCP controller has eliminated this concern, while producing all the available fluid from the wellbore without damaging the pump. With this automated technology and a web-based SCADA system, it became possible to monitor several data trends and discover additional control algorithms. Further development of these algorithms is ongoing. Pump-Off Control for PCPs Two main objectives were defined when the theory of the PCP controller was introduced. The first objective was to reduce premature pump failure. The most common failure is stator damage due to pump-off. Pump-off is defined as a lack of fluid entry into the pump. This causes a lack of lubrication to the stator, resulting in extremely high temperatures being generated. The high temperature ultimately burns the elastomer in the pump. The stator’s rubber surface becomes hard, brittle, and cracked. In severe circumstances, the stator contour is torn up, producing rubber at surface. This may be caused by one or a combination of the following problems: plugged pump intake, poor inflow, or production rates exceeding inflow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".